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Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

R. Surendran1,*, Youseef Alotaibi2, Ahmad F. Subahi3

1 Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
2 Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
3 Department of Computer Science, University College of Al Jamoum, Umm Al-Qura University, Makkah, 21421, Saudi Arabia

* Corresponding Author: R. Surendran. Email: email

Computer Systems Science and Engineering 2023, 46(1), 835-851. https://doi.org/10.32604/csse.2023.036757

Abstract

Recently, wireless sensor networks (WSNs) find their applicability in several real-time applications such as disaster management, military, surveillance, healthcare, etc. The utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and governments. Real-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor nodes. Therefore, the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the network. In this aspect, this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering (LOWGO-EAC) scheme for WSN-assisted real-time disaster management. The major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster regions. To achieve this, the LOWGO-EAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning (LOBL) concept with the traditional WGO algorithm to improve the convergence rate. In addition, the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy (RE), distance to the base station (BS) (DBS), and node degree (ND). The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management scenarios. The experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

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APA Style
Surendran, R., Alotaibi, Y., Subahi, A.F. (2023). Lens-oppositional wild geese optimization based clustering scheme for wireless sensor networks assists real time disaster management. Computer Systems Science and Engineering, 46(1), 835-851. https://doi.org/10.32604/csse.2023.036757
Vancouver Style
Surendran R, Alotaibi Y, Subahi AF. Lens-oppositional wild geese optimization based clustering scheme for wireless sensor networks assists real time disaster management. Comput Syst Sci Eng. 2023;46(1):835-851 https://doi.org/10.32604/csse.2023.036757
IEEE Style
R. Surendran, Y. Alotaibi, and A.F. Subahi, “Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management,” Comput. Syst. Sci. Eng., vol. 46, no. 1, pp. 835-851, 2023. https://doi.org/10.32604/csse.2023.036757



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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